store = {}
store['args']={'name': 'bald_mnist_1030548', 'type': 'AcquisitionFunction.bald', 'seed': 1030548, 'experiment_description': 'Coreset BALD vs BALD', 'acquisition_method': 'AcquisitionMethod.independent', 'available_sample_k': 1, 'num_inference_samples': 20, 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'target_accuracy': 0.96, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.mnist', 'initial_samples': [38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329], 'experiment_task_id': 10, 'experiments_laaos': './experiment_configs/coreset_bald_vs_bald/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=10', '--experiments_laaos=./experiment_configs/coreset_bald_vs_bald/configs.py']
store['iterations']=[]
store['initial_samples']=[38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6483, 'nll': 2.650308056640625}, 'chosen_samples': ['19244'], 'chosen_samples_score': [1.3060043251031794], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6224, 'nll': 2.964466689682007}, 'chosen_samples': ['11025'], 'chosen_samples_score': [1.3138728740346999], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6666, 'nll': 2.305193370819092}, 'chosen_samples': ['54553'], 'chosen_samples_score': [1.2286579297738163], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6797, 'nll': 2.2569631954193117}, 'chosen_samples': ['4342'], 'chosen_samples_score': [1.3658544569911144], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6972, 'nll': 2.0440265113830565}, 'chosen_samples': ['41572'], 'chosen_samples_score': [1.2295470081076556], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6644, 'nll': 2.2917089824676515}, 'chosen_samples': ['43565'], 'chosen_samples_score': [1.1738152614730661], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6639, 'nll': 2.3393466464996338}, 'chosen_samples': ['32473'], 'chosen_samples_score': [1.2309443237139597], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6799, 'nll': 2.208951461791992}, 'chosen_samples': ['36150'], 'chosen_samples_score': [1.1805757595005724], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7142, 'nll': 1.885044729232788}, 'chosen_samples': ['12117'], 'chosen_samples_score': [1.1317906780033327], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6332, 'nll': 2.449151675415039}, 'chosen_samples': ['24072'], 'chosen_samples_score': [1.2231549969472777], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.681, 'nll': 2.1919538421630858}, 'chosen_samples': ['20700'], 'chosen_samples_score': [1.1746078992079583], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6823, 'nll': 1.9512670391082763}, 'chosen_samples': ['52298'], 'chosen_samples_score': [1.0827094407129034], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6791, 'nll': 2.0910987852096556}, 'chosen_samples': ['46620'], 'chosen_samples_score': [1.0911346336272483], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6973, 'nll': 1.788362530708313}, 'chosen_samples': ['52314'], 'chosen_samples_score': [1.0629822828993176], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7317, 'nll': 1.6387787796020508}, 'chosen_samples': ['8584'], 'chosen_samples_score': [1.0938293589304013], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6984, 'nll': 1.7340985450744628}, 'chosen_samples': ['29872'], 'chosen_samples_score': [1.1113753629183534], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7168, 'nll': 1.7394273551940918}, 'chosen_samples': ['59726'], 'chosen_samples_score': [1.1723145049164208], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6912, 'nll': 1.6914029903411865}, 'chosen_samples': ['40905'], 'chosen_samples_score': [1.1791258473237218], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.699, 'nll': 1.7351879322052002}, 'chosen_samples': ['48540'], 'chosen_samples_score': [1.1206543449920987], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6485, 'nll': 1.7596774513244628}, 'chosen_samples': ['37084'], 'chosen_samples_score': [1.2288900335406034], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6983, 'nll': 1.6487367555618286}, 'chosen_samples': ['33680'], 'chosen_samples_score': [1.1413503946058376], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6973, 'nll': 1.597097578048706}, 'chosen_samples': ['21738'], 'chosen_samples_score': [1.0658530131512602], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7248, 'nll': 1.4599462474822997}, 'chosen_samples': ['21315'], 'chosen_samples_score': [1.1132464821587762], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7191, 'nll': 1.468269130897522}, 'chosen_samples': ['27062'], 'chosen_samples_score': [1.1446577059014946], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7174, 'nll': 1.4325069366455079}, 'chosen_samples': ['27083'], 'chosen_samples_score': [1.0064644818353388], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7019, 'nll': 1.465228528213501}, 'chosen_samples': ['32157'], 'chosen_samples_score': [1.0604386274925577], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7312, 'nll': 1.3408549102783203}, 'chosen_samples': ['38898'], 'chosen_samples_score': [1.0088769271493687], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7382, 'nll': 1.3373746564865112}, 'chosen_samples': ['42517'], 'chosen_samples_score': [1.1119807036835025], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7378, 'nll': 1.391662159729004}, 'chosen_samples': ['51600'], 'chosen_samples_score': [1.046981752904339], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7471, 'nll': 1.3625813735961914}, 'chosen_samples': ['2748'], 'chosen_samples_score': [1.050466708542134], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7837, 'nll': 1.1219371612548827}, 'chosen_samples': ['37542'], 'chosen_samples_score': [1.0303056080135127], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.786, 'nll': 1.1162555841445922}, 'chosen_samples': ['23305'], 'chosen_samples_score': [1.0958182631567581], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7496, 'nll': 1.2518124935150146}, 'chosen_samples': ['58401'], 'chosen_samples_score': [1.1096749214006478], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7855, 'nll': 1.1054496631622315}, 'chosen_samples': ['22364'], 'chosen_samples_score': [0.964300024893814], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.765, 'nll': 1.1998826454162597}, 'chosen_samples': ['49026'], 'chosen_samples_score': [1.0445043820844475], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7616, 'nll': 1.1779490547180176}, 'chosen_samples': ['27130'], 'chosen_samples_score': [1.0084106453306072], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7696, 'nll': 1.187623009109497}, 'chosen_samples': ['47132'], 'chosen_samples_score': [0.9993636131520309], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7643, 'nll': 1.2687051986694335}, 'chosen_samples': ['23262'], 'chosen_samples_score': [1.0957935522080162], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.784, 'nll': 1.052537090110779}, 'chosen_samples': ['25960'], 'chosen_samples_score': [0.8885920644412625], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7743, 'nll': 1.2161152376174926}, 'chosen_samples': ['23391'], 'chosen_samples_score': [1.031851153920444], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8196, 'nll': 0.9547985227584839}, 'chosen_samples': ['17742'], 'chosen_samples_score': [0.9002777277662289], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7997, 'nll': 1.0489591844558717}, 'chosen_samples': ['47365'], 'chosen_samples_score': [1.0125655623284313], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8269, 'nll': 0.9434095401763916}, 'chosen_samples': ['35239'], 'chosen_samples_score': [0.9261172417600498], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.815, 'nll': 0.9460148910522461}, 'chosen_samples': ['35946'], 'chosen_samples_score': [0.8836889544254463], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8146, 'nll': 0.9581825748443603}, 'chosen_samples': ['9118'], 'chosen_samples_score': [0.896965375874807], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8085, 'nll': 0.9740157974243164}, 'chosen_samples': ['56037'], 'chosen_samples_score': [0.9414669764488156], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.795, 'nll': 1.0025635627746583}, 'chosen_samples': ['54556'], 'chosen_samples_score': [0.9044911757440374], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7843, 'nll': 1.050772889137268}, 'chosen_samples': ['57186'], 'chosen_samples_score': [1.0150424123274857], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8391, 'nll': 0.9023682029724122}, 'chosen_samples': ['22256'], 'chosen_samples_score': [0.9836829800803478], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8094, 'nll': 0.9454700769424439}, 'chosen_samples': ['33505'], 'chosen_samples_score': [0.8724584980819188], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8061, 'nll': 0.9134341247558594}, 'chosen_samples': ['1420'], 'chosen_samples_score': [0.8831934359798117], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8322, 'nll': 0.9131286985397339}, 'chosen_samples': ['28102'], 'chosen_samples_score': [0.9463650514342702], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8523, 'nll': 0.8860245164871215}, 'chosen_samples': ['54186'], 'chosen_samples_score': [1.0690488238858529], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8284, 'nll': 0.8957086410522461}, 'chosen_samples': ['39150'], 'chosen_samples_score': [0.825634208515153], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8044, 'nll': 0.9407503517150879}, 'chosen_samples': ['40526'], 'chosen_samples_score': [0.7958741070613078], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8019, 'nll': 0.9711490858078002}, 'chosen_samples': ['23226'], 'chosen_samples_score': [0.9260345968325185], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8305, 'nll': 0.8821250816345215}, 'chosen_samples': ['27317'], 'chosen_samples_score': [0.8478449140850923], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8447, 'nll': 0.8678054204940796}, 'chosen_samples': ['13030'], 'chosen_samples_score': [0.8421402577929343], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8151, 'nll': 0.9109161283493042}, 'chosen_samples': ['54065'], 'chosen_samples_score': [0.8945905548970949], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8654, 'nll': 0.8146006635665893}, 'chosen_samples': ['16922'], 'chosen_samples_score': [1.1206340557990138], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8104, 'nll': 0.9268296443939209}, 'chosen_samples': ['10028'], 'chosen_samples_score': [0.8588186084230678], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.829, 'nll': 0.8901452728271484}, 'chosen_samples': ['25844'], 'chosen_samples_score': [0.8286728722623846], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8667, 'nll': 0.7879061851501464}, 'chosen_samples': ['3719'], 'chosen_samples_score': [1.1134134789355994], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.86, 'nll': 0.8880531711578369}, 'chosen_samples': ['44095'], 'chosen_samples_score': [1.161933237236651], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8574, 'nll': 0.8672172019958496}, 'chosen_samples': ['47506'], 'chosen_samples_score': [1.0862703767874478], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8751, 'nll': 0.7307112316131592}, 'chosen_samples': ['6873'], 'chosen_samples_score': [1.048988315380746], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8713, 'nll': 0.7662609860420228}, 'chosen_samples': ['34847'], 'chosen_samples_score': [1.0625849650928234], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8801, 'nll': 0.7560877494812012}, 'chosen_samples': ['52968'], 'chosen_samples_score': [1.148085405840725], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8808, 'nll': 0.7173495235443115}, 'chosen_samples': ['55526'], 'chosen_samples_score': [1.0689389356731938], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8931, 'nll': 0.6499775975227357}, 'chosen_samples': ['23152'], 'chosen_samples_score': [1.03349265763983], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.892, 'nll': 0.681090347957611}, 'chosen_samples': ['6944'], 'chosen_samples_score': [1.0587405234032647], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9005, 'nll': 0.6627483681678772}, 'chosen_samples': ['38974'], 'chosen_samples_score': [1.0194960541452376], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8913, 'nll': 0.6500514290809631}, 'chosen_samples': ['5474'], 'chosen_samples_score': [1.0482972084007485], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8951, 'nll': 0.6630426362991333}, 'chosen_samples': ['39668'], 'chosen_samples_score': [1.1354693774436093], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8952, 'nll': 0.6290003748893738}, 'chosen_samples': ['24424'], 'chosen_samples_score': [1.0251253509306353], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8944, 'nll': 0.632884449005127}, 'chosen_samples': ['49525'], 'chosen_samples_score': [1.0647371816417301], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8899, 'nll': 0.7100356519699097}, 'chosen_samples': ['34060'], 'chosen_samples_score': [1.2123570268570605], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8917, 'nll': 0.6407103943824768}, 'chosen_samples': ['19942'], 'chosen_samples_score': [1.0520484426928276], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8949, 'nll': 0.6509964874267579}, 'chosen_samples': ['6174'], 'chosen_samples_score': [1.0500363268374708], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8955, 'nll': 0.6477330171585083}, 'chosen_samples': ['23997'], 'chosen_samples_score': [1.1553538171596873], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8773, 'nll': 0.6579572043418884}, 'chosen_samples': ['42415'], 'chosen_samples_score': [0.9814886227466499], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8869, 'nll': 0.6998281112670899}, 'chosen_samples': ['56314'], 'chosen_samples_score': [1.0904257947444953], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8892, 'nll': 0.639292576789856}, 'chosen_samples': ['32427'], 'chosen_samples_score': [0.9471257591470497], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9045, 'nll': 0.6148065584182739}, 'chosen_samples': ['14619'], 'chosen_samples_score': [1.013878732776617], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9028, 'nll': 0.6027406439781189}, 'chosen_samples': ['21532'], 'chosen_samples_score': [1.0579398125113613], 'chosen_samples_orignal_score': None})
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